The Social Hourglass: Enabling Socially-aware Applications

and Services

Adriana IamnitchiUniversity of South Florida

anda@cse.usf.edu

Much Social Information Available

• Connects people through relationships– Object centric: use of same objects– Person centric: declared relationships or co-

participation in events, groups, etc.

Mining Social Data• Spam filtering• Sybil identification• Personalized search• Target marketing• Medical emergency notifications• …

Current Approach: Vertically Integrated Socially-aware

Applications

Data Source

ApplicationApplication

Data Source

Data Source

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Challenges with Current Approach

• Application-limited collection and use of social information– High bootstrap cost– Limited (potentially inaccurate)

information. E.g., Information from online social networks

• Hidden incentives to have many “friends”• All relationships equal• Symmetric relationships

• Newer proposals to merge different sources of social (and sensor) information for one app– Specifically targeting context awareness

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Motivating Application: CallCensor

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Motivating Application: Sofa Surfer

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Motivating Application: Data Placement

Proposal: An Infrastructure for Social Computing

Sofa SurferRoommate Finder

CallCensor

…

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ObjectiveAn infrastructure that:• Can fuse information from various

sources• Allow user to control own information

– What is collected– Where it is stored– Who can access it

• Provide social knowledge to a variety of applications:– Social inferences (may be non-trivial)

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Outline

• Motivation• The Social Hourglass architecture• Social Sensors (work in progress)• Personal Aggregator (some ideas)• Social Knowledge Service: Prometheus

(Kourtellis et al, Middleware 2010)– Data Management– API for social inferences– Experimental evaluation (on PlanetLab)

• Summary

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The Social Hourglass Architecture

Applications

Social Inference API

Social Data ManagementPersonal Aggregators

Social Sensors

Social Signals

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Social Sensors

Consume existing social signals• Location• Collocation• Schedule (e.g., Google calendar)• Mobile phone activity (calls, sms)• Online social network

interactions• Email• Personal relations (family)• Shared content• Shared interest (e.g., CiteULike)• …

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Social Sensors• Report on behalf of ego:

– Alter, the person ego is interacting with– An activity tag: e.g., “outdoors”, “dining”

• Based on content, location, predefined labels, etc.– A weight: e.g., 0.15

• Run on ego’s mobile devices, desktop, or on web

• Processes user interactions– To reduce noise– To distinguish between routine and meaningful

interactions

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Social Sensors: Challenges • Identifying activity tags:

– Mine text for keywords (emails, sms, blogs, etc)

– Reverse geo-coding to find where (co)located

– Predefined labels or dictionary and ontologies

• Quantifying interactions (assigning weights):– Frequency, duration, time in-between

interactions– Familiar strangers versus active social

interaction

Work in Progress: Social Sensor for Gaming Interactions

• Variability in playing habits• Variability in playing skills• Time patterns

Aggregators• Act as the user’s personal assistant• Runs on trusted device (cell phone)• Responsible for

– Managing passwords for various applications

– Personalization– Identity management

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The Social Hourglass Architecture

Applications

Social Inference API

Social Data ManagementPersonal Aggregators

Social Sensors

Social Signals

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Social Graph

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Prometheus• Peer-to-peer architecture

– Users contribute resources (peers)– Fundamental change from typical peer-to-peer

networks: not every user has its peer• Input: Social information collected from

different social sensors (reported via aggregators)

• Output: Social information made available to applications and services– Information made available subject to user

policies

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Distributed Social Graph

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Prometheus Architecture

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Architecture Details• Users have a unique user ID • Select trusted peer group based on

offline social trust with peer owners• A user’s trusted peers communicate

via Scribe• Only the user’s trusted peers can

decrypt user’s social data and thus perform social inference functions

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Social Data Protection• 2 sets of public/private keys

– User’s– User’s trusted peer group

• Social sensors submit data encrypted with the group’s public key and signed with the user’s private key– Access to user’s private key only on user’s devices– Data stored in the Pastry overlay

• Only trusted peers can decrypt and authenticate data

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Social Inference FunctionsThe social graph management service exports an API

that implement social inferences

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API for Applications: Social Inference Functions

• 5 basic social inference functions:• relation_test (ego, alter, ɑ, w)• top_relations (ego, ɑ, n)• neighborhood (ego, ɑ, w, radius)• proximity (ego, ɑ, w, radius,

distance)• social_strength (ego, alter)

• More complex functions can be built

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Social Strength• Quantifies strength between ego and

alter• Result normalized to consider overall

activity• Search all paths of maximum 2 social

hops• One approach to quantify social

strength. Others are certainly possible.

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Lessons from Experiments on PlanetLab

• Social-based mapping of users onto peers leads to significant performance gains:– More than 15% of requests finish faster – An order of magnitude fewer messages

• Reasonable latency– Code significantly improved since

publication in Middleware 2010

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Experimental Results: Neighborhood Requests

10 users per peer 50 users per peerPrometheus: User-Controlled P2P Social Data Management for Socially-Aware Applications, Nicolas Kourtellis, Joshua Finnis, Paul Anderson, Jeremy Blackburn, Cristian Borcea, Adriana Iamnitchi. 11th International Middleware Conference, Bangalore, India, November 2010.

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Real Social Traces: NJIT Social Graph

100 randomly selected students from NJIT given Bluetooth-enabled phones that report their collocation

• Data recorded– Collocation with two

thresholds (45 and 90 minutes)

– Facebook friendships• Sparse graph

(commuters)

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CallCensor• CallCensor implemented on Android

– Cell phone silenced, rings or vibrates depending on the social context and relationship with caller

– Relationship with caller: • Social strength > threshold: allow call• Caller directly connected by work• Caller connected by work and ≤ 2 hops away

• Real social data from 100 users stored on 3 nodes from PlanetLab

• Real time performance constraints

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Lessons from CallCensor Experiments

• Vulnerability to malicious users mitigated by directed, multi-edged, weighted social graph

• Vulnerability to malicious peers related to social graph distribution

• Peers gain the properties of the social graph they represent

Resilience to (Social) Attacks

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Summary• The social hourglass architecture• Prometheus: a decentralized service that

enables socially-aware applications and services by collecting, managing and exposing social knowledge, subject to user-specified privacy policies.

• Unique contributions:– Social graph representation– Aggregated social data – Social inference functions– Socially-aware design

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Much Work to Be Done• Developing social sensors• Aggregator:

– proof of concept implementation– Performance

• Evaluating benefits of social knowledge in system design

• Socially-aware applications• Query language for social inferences• Privacy protection

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More Information• The Social Hourglass: an Infrastructure for

Socially-aware Applications and Services, Iamnitchi et al., IEEE Internet Computing, May/June 2012

• Prometheus: User-Controlled P2P Social Data Management for Socially-Aware Applications, Kourtellis et al., Middleware 2010

• Vulnerability in Socially-Informed Peer-to-Peer System, Jeremy Blackburn, Nicolas Kourtellis, and Adriana Iamnitchi. Fourth Workshop on Social Network Systems (SNS 2011)

http://www.cse.usf.edu/~andaanda@cse.usf.edu

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Acknowledgements• My team of talented graduate

students and alumni:

• US National Science Foundation grants CNS-0831785 and CNS-0952420

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Thank you!

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Neighborhood Inference

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Social Strength Inference

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A Distributed System

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Or a Distributed System

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An Example: Interest Sharing

“No 24 in B minor, BWV 869”“Les Bonbons”

“Yellow Submarine”“Les Bonbons”

“Yellow Submarine”“Wood Is a Pleasant Thing to Think About”

“Wood Is a Pleasant Thing to Think About”

The interest-sharing graph GmT(V, E):

V is set of users active during interval T An edge in E connects users who share at least m file

requests within T

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Small Worlds

Word co-occurrences

Film actors

LANL coauthors

Internet

Web

Food web

Power grid

D. J. Watts and S. H. Strogatz, Collective dynamics of small-world networks. Nature, 393:440-442, 1998R. Albert and A.-L. Barabási, Statistical mechanics of complex networks, R. Modern Physics 74, 47 (2002).

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Web Interest-Sharing Graphs

7200s, 50files

3600s, 50files

1800s, 100files

1800s, 10file

300s, 1file

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DØ Interest-Sharing Graphs

7days, 1file

28 days,1 file

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KaZaA Interest-Sharing Graphs

7day, 1file

28 days1 file

2 hours1 file

1 day2 files

4h2 files

12h4 files

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Proactive Information Dissemination

D0

WebKazaa